A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to r...

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Bibliographic Details
Main Authors: Francesco Gentile, Evelina La Civita, Bartolomeo Della Ventura, Matteo Ferro, Dario Bruzzese, Felice Crocetto, Pierre Tennstedt, Thomas Steuber, Raffaele Velotta, Daniela Terracciano
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Cancers
Subjects:
artificial neural network
Phi
PCLX
prostate cancer
tumor markers
Online Access:https://www.mdpi.com/2072-6694/15/5/1355

Internet

https://www.mdpi.com/2072-6694/15/5/1355

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